Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter
Joshua Hinz, Valentin V. Karasiev, S. X. Hu, Deyan Mihaylov
Abstract
In this paper $\mathrm{\ensuremath{\Delta}}$ learning is used to map orbital-free density functional theory (DFT) ionic forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the approximate force difference in terms of the ion positions is constructed and serves as a stand-in for the ground truth force difference. Descriptor vectors for ion configurations are constructed using all distances between ions in conjunction with an indexing based on a nearest neighbor ranking. It is demonstrated that such a scheme of descriptors can uniquely describe an ionic configuration up to a rotation and reflection when no ambiguity in the nearest neighbor ranking exists. How to handle the case when an ambiguity exists in the nearest neighbor ranking is discussed. As a proof of principle, the model is trained and tested on warm dense hydrogen at temperatures between 1 and 15 eV. Once tested, the model was used to perform molecular dynamic simulations of warm dense hydrogen. The resulting energies and pressures are within 1 and 2% of their respective target KS values.